知识与财富的链接
近年来,卷积神经网络（Convolutional neural networks,CNN）已在图像理解领域得到了广泛的应用,引起了研究者的关注. 特别是随着大规模图像数据的产生以及计算机硬件（特别是GPU）的飞速发展,卷积神经网络以及其改进方法在图像理解中取得了突破性的成果,引发了研究的热潮. 本文综述了卷积神经网络在图像理解中的研究进展与典型应用. 首先,阐述卷积神经网络的基础理论;然后,阐述其在图像理解的具体方面,如图像分类与物体检测、人脸识别和场景的语义分割等的研究进展与应用.
Convolutional neural networks (CNN) have been widely applied to image understanding, and they have arose much attention from researchers. Specifically, with the emergence of large image sets and the rapid development of GPUs, convolutional neural networks and their improvements have made breakthroughs in image understanding, bringing about wide applications into this area.
This paper summarizes the up-to-date research and typical applications for convolutional neural networks in image understanding. We firstly review the theoretical basis, and then we present the recent advances and achievements in major areas of image understanding, such as image classification, object detection, face recognition, semantic image segmentation etc.
